{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import visuals as vs\n",
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series([], dtype: float64)\n"
     ]
    }
   ],
   "source": [
    "train_data = pd.read_csv('final_train_data.csv')\n",
    "\n",
    "train_label = train_data['SalePrice']\n",
    "train_data.drop(['SalePrice'],axis = 1,inplace = True)\n",
    "\n",
    "missing = train_data.columns[train_data.isnull().any()].tolist()\n",
    "print(train_data[missing].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import  RidgeCV,LassoCV\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(train_data,train_label,random_state=0,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of RidgeCV on train is 0.92238083416\n",
      "The r2 score of RidgeCV on test is 0.905990657591\n"
     ]
    }
   ],
   "source": [
    "#设置超参数（正则参数）范围\n",
    "alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "#生成一个RidgeCV实例\n",
    "ridge = RidgeCV(alphas=alphas, store_cv_values=True)\n",
    "ridge.fit(x_train,y_train.ravel())\n",
    "\n",
    "y_train_pred_ridge = ridge.predict(x_train)\n",
    "y_test_pred_ridge = ridge.predict(x_test)\n",
    "\n",
    "print ('The r2 score of RidgeCV on train is', r2_score(y_train, y_train_pred_ridge))\n",
    "print ('The r2 score of RidgeCV on test is', r2_score(y_test, y_test_pred_ridge))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 在训练集上观察预测残差的分布，看是否符合模型假设：噪声为0均值的高斯噪声"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3efJkWltb8TDk0DFmzBgmT57cJ+vqye/IDwFOAh6IiMVV2bnUAvz6iDgd+Hfg49W8n1E7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\nmEEuSVLBDHJJkgr2/wETziX67sjKdgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17b39741780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(7, 5)) \n",
    "f.tight_layout() \n",
    "ax.hist(y_train - y_train_pred_ridge,bins=40, label='Residuals Linear', color='b', alpha=.5); \n",
    "ax.set_title(\"Histogram of Residuals\") \n",
    "ax.legend(loc='best');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LassoCV on train is 0.919458207534\n",
      "The r2 score of LassoCV on test is 0.909838060603\n"
     ]
    }
   ],
   "source": [
    "#生成一个LassoCV实例s\n",
    "lasso = LassoCV(alphas=np.logspace(-3, 2, 5))\n",
    "lasso.fit(x_train, y_train.ravel())\n",
    "\n",
    "y_train_pred_lasso = lasso.predict(x_train)\n",
    "y_test_pred_lasso = lasso.predict(x_test)\n",
    "\n",
    "print ('The r2 score of LassoCV on train is', r2_score(y_train, y_train_pred_lasso))\n",
    "print ('The r2 score of LassoCV on test is', r2_score(y_test, y_test_pred_lasso))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 在训练集上观察预测残差的分布，看是否符合模型假设：噪声为0均值的高斯噪声"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jqukZwOuqZfar1rkROKma/iZwbjX9ceCb1fRJwMJt7WOw26SH9moFPtNN+YC3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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17b396716d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(7, 5)) \n",
    "f.tight_layout() \n",
    "ax.hist(y_train - y_train_pred_lasso,bins=40, label='Residuals Linear', color='b', alpha=.5); \n",
    "ax.set_title(\"Histogram of Residuals\") \n",
    "ax.legend(loc='best');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 学习曲线\n",
    "\n",
    "#### 当alpha = 10的时候。训练和验证集上的误差最小，同时r2-score基本在0.91."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py:418: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
      "  \"matplotlib is currently using a non-GUI backend, \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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N4NxWXvK4EKJaCHEAwO8A3KxbVyqEeFEIEQTwMoACyK4SEEK8L4TYK6TPAHwMeRLQlgCA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17b399785f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vs.ModelLearning(x_train,y_train.ravel())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
